3D-high efficiency video coding (3D-HEVC) is an extension of the high efficiency video coding (HEVC) standard for the compression of the texture videos and depth maps. In 3D-HEVC inter-coding, the coding unit (CU) is recursively performed on variable sizes, namely, depth levels. The CU size decision process is conducted using all the possible depth levels to obtain the one with the least rate-distortion (RD) cost using the Lagrange multiplier. These tools achieve the highest coding efficiency but incur a very high computational complexity. In this paper, a fast CU size decision algorithm is proposed to reduce the complexity caused by the CU size splitting process. The proposed algorithm is based on the CU homogeneity classification using machine learning technology. First, the tensor feature is extracted to characterize the homogeneity of CU, which has a strong relationship with CU sizes. Then, a boosted decision stump algorithm is employed to analyze and construct a binary classification model from the extracted features and find suitable thresholds for the proposed method. Finally, an efficient early termination of CU splitting is released based on adaptive thresholds for texture videos and depth maps. The experimental results show that the proposed algorithm reduces a significant encoding time, while the loss in coding efficiency is negligible.

3D高效率视频编码（3D-HEVC）是高效视频编码（HEVC）标准的扩展，用于压缩纹理视频和深度图。在3D-HEVC帧间编码中，对可变大小（即深度水平）递归地执行编码单元（CU）。使用所有可能的深度级别执行CU大小决策过程，以使用拉格朗日乘数获得具有最小速率失真（RD）成本的深度级别。这些工具可实现最高的编码效率，但会带来很高的计算复杂性。本文提出了一种快速的CU大小决策算法，以减少CU大小拆分过程所引起的复杂性。所提出的算法基于使用机器学习技术的CU均匀性分类。首先，提取张量特征以表征CU的均匀性，与CU大小有很强的关系。然后，采用增强的决策树桩算法，从提取的特征中分析和构建二进制分类模型，并为该方法找到合适的阈值。最终，基于纹理视频和深度图的自适应阈值，发布了CU拆分的有效早期终止。实验结果表明，该算法减少了可观的编码时间，而编码效率的损失可忽略不计。基于纹理视频和深度图的自适应阈值，可有效释放CU分裂的早期终止。实验结果表明，该算法减少了可观的编码时间，而编码效率的损失可忽略不计。基于纹理视频和深度图的自适应阈值，可有效释放CU分裂的早期终止。实验结果表明，该算法减少了可观的编码时间，而编码效率的损失可忽略不计。